Field of the Invention
Embodiments of the invention provide techniques for analyzing a sequence of video frames. More specifically, embodiments of the invention relate to techniques for observing and identifying anomalous behavior in a sequence of video frames using adaptive voting experts.
Description of the Related Art
Some currently available video surveillance systems provide simple object recognition capabilities. For example, a video surveillance system may be configured to classify a group of pixels (referred to as a “blob”) in a given frame as being a particular object (e.g., a person or vehicle). Once identified, a “blob” may be tracked from frame-to-frame in order to follow the “blob” moving through the scene over time, e.g., a person walking across the field of vision of a video surveillance camera. Further, such systems may be configured to determine when an object has engaged in certain predefined behaviors. For example, the system may include definitions used to recognize the occurrence of a number of pre-defined events, e.g., the system may evaluate the appearance of an object classified as depicting a car (a vehicle-appear event) coming to a stop over a number of frames (a vehicle-stop event). Thereafter, a new foreground object may appear and be classified as a person (a person-appear event) and the person then walks out of frame (a person-disappear event). Further, the system may be able to recognize the combination of the first two events as a “parking-event.”
However, such surveillance systems typically require that the objects and/or behaviors which may be recognized by the system to be defined in advance. Thus, in practice, these systems rely on predefined definitions for objects and/or behaviors to evaluate a video sequence. In other words, unless the underlying system includes a description for a particular object or behavior, the system is generally incapable of recognizing that behavior (or at least instances of the pattern describing the particular object or behavior). Thus, what is “normal” or “anomalous” is defined in advance and separate software products are required to recognize additional objects or behaviors. This results in video surveillance systems with recognition capabilities that are labor intensive and prohibitively costly to maintain or adapt for different specialized applications. Accordingly, currently available video surveillance systems are typically unable to recognize new patterns of behavior that may emerge in a given scene or recognize changes in existing patterns. More generally, such systems are often unable to identify objects, events, behaviors, or patterns (or classify such objects, events, behaviors, etc., as being normal or anomalous) by observing what happens in the scene over time; instead, such systems rely on static patterns defined in advance.